Personalizing HMI elements in ADAS using ontology meta-models and rule based reasoning

Abstract

Advanced Driver Assistant systems (ADAS) are receiving increased research focus as they promote a safer and more comfortable driving experience. In this context, personalization can play a key role as the different driver/rider needs, the environmental context and driver’s/rider’s state can be taken into account towards delivering custom tailored interaction and performing intelligent decision making. This paper presents an ontology-based approach for personalizing Human Machine Interaction (HMI) elements in ADAS systems. The main features of the presented research work include: (a) semantic modelling of relevant data in the form of an ontology meta-model that includes the driver/ rider information, the vehicle and its HMI elements, as well as the external environment, (b) rule-based reasoning on top of the meta-model to derive appropriate personalization decisions, and (c) adaptation of the vehicle’s HMI elements and interaction paradigms to best fit the particular driver or rider, as well as the overall driving context.